Validation of a teaching model instrument for university education in Ecuador through an artificial intelligence algorithm

Jenniffer Sobeida Moreira-Choez ^1* Tibisay Milene Lamus de Rodríguez ² Aracelly Fernanda Núñez-Naranjo ³ Ángel Ramón Sabando-García ⁴ María Belén Reinoso-Ávalos ⁵ Cynthia Michel Olguín-Martínez ⁶ Daniel Omar Nieves-Lizárraga ⁶ Julieta Elizabeth Salazar-Echeagaray ⁶

• ¹ Coordination of Mathematics and Statistics of the Faculty of Basic Sciences, Technical University of Manabi, Portoviejo, Ecuador
• ² State University of Milagro, Milagro, Guayas, Ecuador
• ³ Universidad Iberoamericana del Ecuador, Quito, Pichincha, Ecuador
• ⁴ Pontifical Catholic University of Ecuador Santo Domingo, Santo Domingo, Ecuador
• ⁵ Unidad Educativa Nuevo Ecuador, Quito, Ecuador
• ⁶ Universidad Autónoma de Sinaloa, Mazatlán, Sinaloa, Mexico

In the context of university education in Ecuador, the application of Artificial Intelligence (AI) for the assessment and adaptation of teaching models marks significant progress towards enhancing educational quality. This study focused on the validation of an AI-based instrument, designed for the evaluation and adaptation of pedagogical strategies in the Ecuadorian university environment. A quantitative methodology was adopted, employing multivariate statistical analyses and structural equation models to investigate the internal consistency, validity, and interrelations among various pedagogical dimensions. A significant sample of teachers, from undergraduate and graduate levels, was analyzed. The findings indicated outstanding internal consistency and discriminative ability among the analyzed teaching models, verifying their feasibility and relevance in the educational field. These results highlight the importance of incorporating innovative and technologically supported didactic methodologies in higher education, underscoring the crucial role of AI in the analysis and interpretation of educational information. Therefore, this study not only confirms the validity of various teaching models through advanced statistical methods but also emphasizes their positive influence on enhancing teaching and learning processes.In accordance with the reviewer's observation, the keywords presented in the final section of the manuscript were revised to ensure they accurately reflect the latent variables of the proposed theoretical model. Specifically, general terms were replaced with precise descriptors representing the five evaluated teaching models (traditional, collaborative, spontaneist, constructivist, and technological), as well as concepts related to artificial intelligence applied to teaching, pedagogical practices, and the evaluation of educational models. This adjustment enhances the terminological coherence of the article, strengthens its visibility in scientific databases, and enables more accurate indexing based on the dimensions analyzed in the study.